Discovering Perceptions in Online Social Media: A Probabilistic Approach

Abstract

People across the world habitually turn to online social media to share their experiences, thoughts, ideas, and opinions as they go about their daily lives. These posts collectively contain a wealth of insights into how masses perceive their surroundings. Therefore, extracting people's perceptions from social media posts can provide valuable information about pertinent issues such as public transportation, emergency conditions, and even reactions to political actions or other activities. This paper proposes a novel approach to extract such perceptions from a corpus of social media posts originating from a given broad geographical region. The approach divides the broad region into a number of sub-regions, and trains language models over social media conversations within these sub-regions. Using Bayesian and geo-smoothing methods, the ensemble of language models can be queried with phrases embodying a perception. Discrete and continuous visualization methods represent the extent to which social media posts within the sub-regions express the query. The capabilities of the perception mining approach are illustrated using transportation-themed scenarios.